10
Exploiting Beacons for Scalable Broadcast Data Dissemination in VANETs Ramon S. Schwartz, Kallol Das, Hans Scholten and Paul Havinga Pervasive Systems (PS), Faculty of Electrical Engineering, Mathematics and Computer Science (EWI) University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands {r.s.schwartz, k.das, hans.scholten, p.j.m.havinga}@utwente.nl ABSTRACT Vehicular Ad-hoc Networks (VANETs) enable the timely broadcast dissemination of event-driven messages to inter- ested vehicles. However, when dealing with broadcast com- munication, suppression techniques must be designed to pre- vent the so-called broadcast storm problem. Numerous sup- pression schemes aim to reduce broadcast redundancy by as- signing vehicles to different delay values, i.e., time slots, that are inversely proportional to their distance to the sender. In this way, only the farthest vehicles would rebroadcast, thereby allowing for quick data dissemination. Despite many efforts, current delay-based schemes still suffer from high levels of contention and collision when the number of vehi- cles rebroadcasting nearly simultaneously is high in dense networks. Even choosing appropriate values for the total number of time slots does not prevent situations where sim- ply no vehicle is assigned to the earliest time slot, what may result in high end-to-end delay. In this paper, we tackle such scalability issues with a scheme that controls with precision the density of vehicles within each time slot. To reach this goal, we exploit the presence of beacons, periodic messages meant to provide cooperative awareness in safety applica- tions. Simulations results show that our protocol outper- forms existing delay-based schemes and is able to dissemi- nate data messages in a scalable, timely, and robust man- ner. Categories and Subject Descriptors C.2.1 [Network Architecture and Design]: Distributed networks, wireless communication General Terms Algorithms, Performance, Reliability Keywords Vehicular Ad-hoc Networks (VANETs), Vehicular Sensor Networks (VSNs), Data Dissemination, Broadcast Storm Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. VANET’12, June 25, 2012, Low Wood Bay, Lake District, UK. Copyright 2012 ACM 978-1-4503-1317-9/12/06 ...$10.00. 1. INTRODUCTION Vehicular Ad-hoc Networks (VANETs) have gained con- siderable attention in the past few years due to their promis- ing applicability with regard to safety, transport efficiency, and entertainment [6]. Vehicles rely on diverse built-in sen- sors to continuously gather, process, and disseminate rele- vant sensor data. For many applications, the data acquired by sensors is of public interest and must be broadcasted (dis- seminated) to all vehicles nearby, e.g., data about accidents. However, several challenges arise when disseminating data based on broadcast communication. Broadcasting with the typical carrier sense multiple access with collision avoidance (CSMA/CA) mechanism present in the 802.11p standard is particularly unreliable due to the lack of acknowledgments. Also, vehicular networks are very dynamic with large de- viations in density depending on the current road traffic. Therefore, protocols designed for VANETs must cope with diverse traffic conditions. In dense networks, a pure flooding scheme results in excessive redundancy, contention, and col- lision rates [11], which is referred to as the broadcast storm problem. Such problem is tackled with broadcast suppression techniques. Conversely, in sparse networks vehicles may face network disconnections when the transmission range em- ployed cannot reach other vehicles farther in the direction of interest. In such scenarios, protocols should also incorpo- rate a store-carry-forward mechanism to take advantage of the mobility of vehicles to store and relay messages until a new opportunity for dissemination emerges. To cope with dense networks, numerous suppression tech- niques aim to assign vehicles to different delay values that are inversely proportional to their distance to the sender. In this way, only the farthest vehicles would rebroadcast, thereby allowing for quick data dissemination [29]. Vehi- cles assigned to delay values sufficiently higher to hear a re- broadcast echo can suppress their transmissions. This sepa- ration in time is accomplished by means of time slots, where each time slot is equivalent to a message’s transmission time. However, in dense networks the number of vehicles rebroad- casting nearly simultaneously in a single time slot can in- crease considerably, thereby still leading to undesirable lev- els of contention and collision [12]. Since time slots match regions within the transmission range of the sender, another problem occurs when there is simply no vehicle assigned to the earliest time slot, what increases the end-to-end delay. In this paper, we propose the Distributed Optimized Time (DOT) slot as a suppression scheme for dense networks. We focus on solving scalability issues of current approaches by controlling with high precision the density of vehicles within 53

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Page 1: Exploiting Beacons for Scalable Broadcast Data Dissemination in … · 2013. 11. 8. · Exploiting Beacons for Scalable Broadcast Data Dissemination in VANETs Ramon S. Schwartz, Kallol

Exploiting Beacons for Scalable Broadcast DataDissemination in VANETs

Ramon S. Schwartz, Kallol Das, Hans Scholten and Paul HavingaPervasive Systems (PS), Faculty of Electrical Engineering, Mathematics and Computer Science (EWI)

University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands{r.s.schwartz, k.das, hans.scholten, p.j.m.havinga}@utwente.nl

ABSTRACTVehicular Ad-hoc Networks (VANETs) enable the timelybroadcast dissemination of event-driven messages to inter-ested vehicles. However, when dealing with broadcast com-munication, suppression techniques must be designed to pre-vent the so-called broadcast storm problem. Numerous sup-pression schemes aim to reduce broadcast redundancy by as-signing vehicles to different delay values, i.e., time slots, thatare inversely proportional to their distance to the sender.In this way, only the farthest vehicles would rebroadcast,thereby allowing for quick data dissemination. Despite manyefforts, current delay-based schemes still suffer from highlevels of contention and collision when the number of vehi-cles rebroadcasting nearly simultaneously is high in densenetworks. Even choosing appropriate values for the totalnumber of time slots does not prevent situations where sim-ply no vehicle is assigned to the earliest time slot, what mayresult in high end-to-end delay. In this paper, we tackle suchscalability issues with a scheme that controls with precisionthe density of vehicles within each time slot. To reach thisgoal, we exploit the presence of beacons, periodic messagesmeant to provide cooperative awareness in safety applica-tions. Simulations results show that our protocol outper-forms existing delay-based schemes and is able to dissemi-nate data messages in a scalable, timely, and robust man-ner.

Categories and Subject DescriptorsC.2.1 [Network Architecture and Design]: Distributednetworks, wireless communication

General TermsAlgorithms, Performance, Reliability

KeywordsVehicular Ad-hoc Networks (VANETs), Vehicular SensorNetworks (VSNs), Data Dissemination, Broadcast Storm

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.VANET’12, June 25, 2012, Low Wood Bay, Lake District, UK.Copyright 2012 ACM 978-1-4503-1317-9/12/06 ...$10.00.

1. INTRODUCTIONVehicular Ad-hoc Networks (VANETs) have gained con-

siderable attention in the past few years due to their promis-ing applicability with regard to safety, transport efficiency,and entertainment [6]. Vehicles rely on diverse built-in sen-sors to continuously gather, process, and disseminate rele-vant sensor data. For many applications, the data acquiredby sensors is of public interest and must be broadcasted (dis-seminated) to all vehicles nearby, e.g., data about accidents.

However, several challenges arise when disseminating databased on broadcast communication. Broadcasting with thetypical carrier sense multiple access with collision avoidance(CSMA/CA) mechanism present in the 802.11p standard isparticularly unreliable due to the lack of acknowledgments.Also, vehicular networks are very dynamic with large de-viations in density depending on the current road traffic.Therefore, protocols designed for VANETs must cope withdiverse traffic conditions. In dense networks, a pure floodingscheme results in excessive redundancy, contention, and col-lision rates [11], which is referred to as the broadcast stormproblem. Such problem is tackled with broadcast suppressiontechniques. Conversely, in sparse networks vehicles may facenetwork disconnections when the transmission range em-ployed cannot reach other vehicles farther in the directionof interest. In such scenarios, protocols should also incorpo-rate a store-carry-forward mechanism to take advantage ofthe mobility of vehicles to store and relay messages until anew opportunity for dissemination emerges.

To cope with dense networks, numerous suppression tech-niques aim to assign vehicles to different delay values thatare inversely proportional to their distance to the sender.In this way, only the farthest vehicles would rebroadcast,thereby allowing for quick data dissemination [29]. Vehi-cles assigned to delay values sufficiently higher to hear a re-broadcast echo can suppress their transmissions. This sepa-ration in time is accomplished by means of time slots, whereeach time slot is equivalent to a message’s transmission time.However, in dense networks the number of vehicles rebroad-casting nearly simultaneously in a single time slot can in-crease considerably, thereby still leading to undesirable lev-els of contention and collision [12]. Since time slots matchregions within the transmission range of the sender, anotherproblem occurs when there is simply no vehicle assigned tothe earliest time slot, what increases the end-to-end delay.

In this paper, we propose theDistributedOptimizedTime(DOT) slot as a suppression scheme for dense networks. Wefocus on solving scalability issues of current approaches bycontrolling with high precision the density of vehicles within

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each time slot. To accomplish this goal, we exploit the pres-ence of beacons, which are messages periodically sent byeach vehicle containing information such as the vehicle’s po-sition and speed. While the use of periodic beacons or hellomessages has been sometimes avoided due to an increasein the network load [29], beacons have been an importanttopic of research and are expected to be massively presentto increase cooperative awareness in safety applications [19].

The remainder of this paper is organized as follows. Sec-tion 2 reviews and outlines problems with current suppres-sion techniques. Next, Section 3 describes DOT in detail.Section 4 describes the performance evaluation of the pro-tocol carried out by means of simulations. Finally, Section5 concludes this paper and outlines our future directions.

2. RELATED WORKVarious broadcast suppression techniques have been pro-

posed to prevent the so-called Broadcast Storm Problem.The ultimate goal is to select only the set with the mini-mum number of vehicles to rebroadcast and disseminate amessage towards the region of interest.

In the context of Mobile Ad-Hoc Networks (MANETs),several solutions to address this problem were proposed andoutlined in [11, 28]. In [28], authors present a comprehen-sive comparison study of various broadcasting techniques inMANETs organized into four categories: (i) simple floodingmethods, without any form of suppression; (ii) probabilitybased methods, that rely on network topology informationto assign a probability for each rebroadcast; (iii) area basedmethods, which use distance information to decide whichnodes should rebroadcast; and (iv) neighbor knowledge meth-ods, which maintain state on the neighborhood via periodichello messages to decide on the next forwarding node. How-ever, these solutions are mostly concerned with providingmeans for route discovery with minimum extra network loadand, therefore, do not take into account the highly dynamicenvironment present on roads, neither exploit specific char-acteristics of vehicular networks such as the predictable mo-bility pattern of vehicles’ movements.

In VANETs, it is generally assumed that each broadcastdata message relates to a certain event of a specific geo-graphical region and, thus, it is targeted mostly to vehiclestraveling through that region. With this goal, protocols thatrely on positioning information falling into categories (iii)and (iv) are most suitable. In category (iii), nodes in theLocation-Based scheme [11] rebroadcast whenever the addi-tional coverage is higher than a pre-defined threshold. Incategory (iv), most protocols require nodes to share 1-hopor 2-hop neighborhood information with other nodes [10, 14,13]. This is particularly not suitable in vehicular environ-ments, since such information can quickly become outdateddue to the high speed of vehicles. In addition, adding neigh-borhood information to periodic messages results in highnetwork overhead. As pointed out in [21], decreasing mes-sage overhead is crucial for leaving sufficient bandwidth foreven-critical messages. In view of these drawbacks, severalprotocols have been proposed specifically for VANET ap-plications. Such protocols present lightweight solutions interms of overhead and elaborate on previous solutions incategory (iii) such as in [11] in order to control, based ondistance, the thresholds determining when vehicles shouldrebroadcast. In the following, we select and describe a few

of these efforts. For a complete survey of solutions, we referthe reader to [12].

The common approach to reduce broadcast redundancyand end-to-end delay in VANETs is to give highest priorityto the most distant vehicles towards the message direction.In [29], three ways of assigning this priority are presented:Weighted p-Persistence, Slotted 1-Persistence and Slottedp-Persistence. In the first scheme, the farthest vehicles re-broadcast with highest probability. In the second approach,vehicles are assigned to different time slots depending ontheir distance to the sender, where vehicles with highestpriority are given the shortest delay before rebroadcasting.Finally, the third approach mixes probability and delay bygiving vehicles with highest priority the shortest delay andhighest probability to rebroadcast. In delay-based schemes,vehicles assigned to later time slots have time to cancel theirtransmissions upon the receipt of an echo. This would bean indication that the information has already been dissem-inated and redundant rebroadcasts can be suppressed. No-tably, to achieve the lowest possible end-to-end delay, de-terministic approaches such as Slotted 1-Persistence shouldbe preferred over probabilistic methods such as Weighted p-Persistence and Slotted p-Persistence. The reason lies in al-ways guaranteeing that the farthest vehicle is chosen, whichis not the case with probabilistic-based methods.

Delay-based schemes have been used in several other workswith the goal of reducing rebroadcast redundancy, e.g., [4, 8,2]. In [4], the Contention-Based Forwarding scheme (CBF)is presented. Authors focus on a distributed delay-basedscheme for mobile ad hoc networks that requires no bea-coning information. In [8], the Urban Multi-hop Broadcast(UMB) protocol is designed to cope with broadcast storm,hidden node, and reliability problems of multi-hop broadcastin urban areas. UMB has a special operation mode for sce-narios with intersections. Nevertheless, it relies on the sametime slotted principle for directional data dissemination. Fi-nally, authors in [2] focus instead on a content-based datadissemination scheme. Information such as data relevance isused to define whether or not a vehicle should rebroadcasta data message. Although with a different primary goal,the proposed protocol is complemented with a delay-basedscheme based on the vehicles’ distance from the sender tolimit the bandwidth used.

Although efficient in tackling the broadcast storm prob-lem, delay-based schemes still present scalability issues whennot employed with optimal parameters. One clear limitationin most schemes proposed is the inability to dynamicallychoose the optimal value for the number and boundaries ofthe time slots used. As shown in Figure 1(a), time slots areusually matched to geographical regions within the transmis-sion range of the sender. However, this can lead to an unevendistribution of vehicles in each time slot. Since transmis-sions in a single time slot occur nearly simultaneously (see[1]) and cannot be canceled, the level of rebroadcast redun-dancy and collision is unnecessarily increased. To cope withcollisions, authors in [24] introduced the concept of microslots to separate in time transmissions assigned to a singletime slot. Another consequence of relying on fixed time slotparameters is that there might simply be no vehicle in oneof the time slots, thereby increasing end-to-end delay of amessage. In this line, the work in [22] introduces a means tocontrol the number of time slots according to the network

54

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sender's transmission

range

sender

t = 0t = stt = 2*st

message direction

(a) Uneven distribution of vehicles among time slots

V1

sender's transmission

range

sender

V4's transmission

range

V3

V2V0

V4

message direction

(b) Sub-optimal vehicle selection in a centralized approach

Figure 1: Overview of problems with current suppression approaches

density. However, authors do not cope with the problem ofnearly simultaneous transmissions in a single time slot.

One way to tackle the problem of uneven distribution ofvehicles among time slots is to adopt a centralized approachfor selecting the next relay vehicle. In [23], the protocol pro-posed aims to classify vehicles into groups and select the re-lay vehicle with the best line-of-sight of each group. In [21],the Emergency Message Dissemination for Vehicular envi-ronments (EMDV) protocol combines both centralized anddistributed approaches. In EMDV, the sender determinesthe next relay vehicle based on neighborhood informationreceived from beacons. The remaining vehicles still follow adelay-based scheme to rebroadcast in case the transmissionfrom the selected vehicle fails. However, one problem arisesin centralized approaches when vehicles transmit messageswith different power levels, as shown in Figure 1(b). In thisscenario, v4 is the farthest vehicle able to rebroadcast themessage received from the sender. However, since v4 em-ployed a lower power level to send its periodic beacons, thesender could not be aware of v4’s presence and mistakenlychooses v3 as the next relay vehicle. The direct consequenceof such mistake is a sub-optimal vehicle selection, leadingto higher end-to-end delays. Finally, authors in [18] aim tosolve these limitations by letting only the farthest (last) ve-hicle rebroadcast with The Last One method (TLO). In casethe last vehicle fails, after a time threshold the protocol re-peatedly defines the next farthest vehicle until the message issuccessfully broadcasted. Although a distributed approachis used in TLO, authors do not discuss how the thresholdvalue is chosen. In addition, they do not present alterna-tives for improving end-to-end delay, e.g., by letting morethan one vehicle rebroadcast in a single time slot in case offailed transmission or inaccurate positioning information.

3. OPTIMIZED TIME SLOT SCHEMEIn this work, we tackle drawbacks of current suppression

techniques with the Distributed Optimized Time (DOT)slot scheme. DOT aims at always selecting the farthest ve-hicles, i.e., optimal relay vehicles, while controlling trans-mission redundancy used to increase robustness. To achievethis goal, DOT has the following characteristics:

- Time slot density control : it exploits positioning infor-mation of 1-hop neighbors to control with precision thetime slots’ boundaries and, therefore, the number of vehi-cles assigned to each time slot. This prevents the unevendistribution of vehicles among time slots (Figure 1(a))when a simple matching of time slots into fixed regionswithin the transmission range of the sender is used. Asa result, transmission redundancy is controlled and end-

to-end delay is kept at a minimum, as there is always avehicle assigned to the earliest time slot.

- Distributed : each vehicle takes the decision regardingwhen to retransmit a message in a distributed fashion.This prevents sub-optimal selections of a relay vehicle asit can occur with a centralized decision (Figure 1(b)).

3.1 Requirements and assumptionsDOT is a suppression scheme that runs on top of the MAC

layer, thereby requiring no modification in the de facto stan-dard for vehicular communication IEEE 802.11p.

The scheme relies on the existence of periodic beaconstransmitted by each vehicle at a certain rate. These beaconsare defined to be transmitted in the form of WAVE ShortMessages (WSMs), according to the IEEE 1609 Family ofStandards for Wireless Access in Vehicular Environments(WAVE) [27, 26]. The IEEE WAVE standard determinesthat these messages carry information such as the data rate,channel number and the transmission power level employed.In addition, contextual information about the vicinity is ex-pected to be included, namely, the vehicle’s geographical po-sition, speed and acceleration [25]. In this work, we assumethat each vehicle is equipped with a device capable of ob-taining the current vehicle’s geographical position, such as aGPS receiver. Therefore, we consider the following messageheader structure: <Vehicle ID, Message ID, Time Stamp,Vehicle’s Geographical Coordinates, Power Level>.

3.2 The protocolBy gathering the information contained in beacons, each

vehicle keeps a table of one-hop neighbors Tn containingthe latest information about the vicinity. Each entry in Tncontains the following information: <Vehicle ID, ExpirationTime, Vehicle’s Geographical Coordinates>. The ExpirationTime field is used to remove vehicles from the table that areno longer in the vicinity. Since there may be failures (e.g.,collisions) when sending these beacons, we introduce a timetolerance before removing an entry defined as tt = 2.5( 1

bf),

where bf is the beaconing rate, e.g., 10 Hz. This accountsfor failure in one beaconing period plus possible extra delay.

The DOT protocol works as follows. Let i be the vehiclesender of message m, and R be the set of vehicles that re-ceived m. Every vehicle j ∈ R schedules a rebroadcast form with a time delay TSij . If any vehicle j ∈ R receives anecho of m before TSij expires, it cancels its rebroadcast andignores future duplicates of m.

The process for defining TSij consists of two main tasks:(i) estimating which vehicles are within the transmissionrange of the sender and received m, i.e., belong to set R;

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sender's transmission

range

sender

t = 0

t = st

t = 2*st

t = 3*st

t = 4*st

t = 5*stmessage direction

(a) tsd = 1

sender's transmission

range

sender

t = 0t = stt = 2*st message direction

t = (0 + d)t = (st + d)t = (2*st + d)

(b) tsd = 2

Figure 2: Examples of different settings for the time slot density parameter

and (ii) sorting the entries of every vehicle j ∈ R in ta-ble Tn based on its geographical position relatively to thesender. The first task is achieved by using the power levelincluded in m when transmitted by i. We elaborate on suchestimation in Section 3.3. In the second task, based on thetransmission range estimation of the sender, each vehicle re-ceiving m makes a list �v with all its neighbors in Tn thatalso belong to set R. These vehicles are then sorted by theirdistance relatively to sender i, where the farthest vehicle isthe first element in �v. In case different vehicles are equallydistant from the sender, they are sorted also by their vehicleID, where lower ID values are placed in front positions in �v.

Figure 3 exemplifies this distributed sorting algorithm. Inthis example, vehicle v1 receives a message from the senderand calculates its order among the neighbors in its table Tnthat may also be in the range of the sender, namely, vehiclesv0, v2 and v3. With the geographical position of these vehi-cles in Tn, v1 sorts these vehicles as �v =< v3, v2, v1, v0 >.

V1: 3rd

sender's transmission

range

sender

V1's transmission

range

V3: 1st

V2: 2ndV0: 4th message direction

Figure 3: Distributed sorting algorithm

With the sorted list of vehicles �v, each vehicle j ∈ Rfinds its own position in �v. We denote this position asSij ∈ [0, n − 1], where n is the total number of elementsin �v. Next, the time that vehicles have to wait before re-broadcasting is given by:

TSij = st

(⌈(Sij + 1)

tsd

⌉− 1

)+ADij , (1)

where the main parameter tsd determines the number ofvehicles that are allowed to be assigned to a single time slot.In other words, this parameter enables the control of timeslots’ density. The slot time st is an estimated value of thetotal time taken for the transmission to complete and themessage be fully received by others, accounting for mediumaccess delay, transmission delay and propagation delay.

Assigning different time slots to vehicles clearly helps breakthe synchronization present in a plain flooding, where allvehicles would rebroadcast nearly simultaneously. However,a similar synchronization on a smaller scale can still occur

when multiple vehicles are assigned to a single time slot.This problem was referred to as the Timeslot Boundary Syn-chronization Problem in [1]. This occurs in our approachwhen tsd > 1. To cope with this problem, we introduce anadditional delay ADij defined as:

ADij = d (Sij mod tsd) , (2)

where d is a time delay sufficiently long for vehicles assignedto the same time slot to sense if other vehicle has alreadystarted its transmission, e.g., DIFS in the MAC 802.11p.

Figure 2 shows how our mechanism works when differentvalues for tsd are used. With tsd = 1, all vehicles in therange of the sender are assigned to individual time slotsbased on their distance to the sender, as shown in Figure2(a). Thus, rebroadcasts are separated in time by multiplesof slot time st. In our second example in Figure 2(b), tsd = 2is used. In this case, two vehicles are assigned to each timeslot. To prevent nearly simultaneous rebroadcasts amongthe two vehicles in each time slot, the vehicle with higherSij value, i.e., nearer to the sender, waits the additionaldelay ADij = d.

With an accurate estimation of set R, optimal results interms of transmission redundancy and end-to-end delay areachieved when tsd = 1. This leads to the minimum num-ber of rebroadcasts and also to the lowest end-to-end de-lay, since only optimal relay vehicles, i.e., farthest vehiclesfrom the previous sender, rebroadcast in the earliest timeslot. Vehicles assigned to later time slots would cancel theirrebroadcasts upon receiving an echo of the message beingpropagated. However, there are a few factors that can pre-vent the optimal estimation of set R, as we discuss in thefollowing section.

3.3 Estimating vehicles in the sender’s rangeAs discussed in Section 3, DOT depends on accurately es-

timating which vehicles are within the transmission range ofthe sender, i.e., belong to set R. On the one hand, underes-timated transmission range values may lead to an excessivenumber of vehicles assigned to earlier time slots. This oc-curs because all vehicles beyond the underestimated rangeare assigned to the first position in list �v. On the other hand,overestimated values may result in longer delays, since vehi-cles unnecessarily wait for the rebroadcast of other vehiclesthat actually did not receive any message.

Just as with many vehicles assigned to a single time slot,underestimating the transmission range can lead to multi-ple vehicles transmitting nearly simultaneously. This mayresult in collisions and mean the end of a message’s dissem-ination. To prevent this effect, we introduce the following

56

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policy. If a vehicle j is beyond the range estimated, it isassigned to the last position in list �v. If �v is empty, j trans-mits immediately after a random small delay taken from theinterval [0, d]. This policy may increase the end-to-end delaybut it maintains the protocol robust against collisions andcontention.

There are two main factors that can affect the estimationof vehicles that belong to set R: (i) error in positioning in-formation and, thus, inaccurate positioning of vehicles in Tn;and (ii) path loss affects in wireless communication such asfree-space loss, shadowing, and Doppler effect. While the ac-curacy of a positioning device such as GPS is generally fixedin the order of a few meters, i.e., 5 meters in outdoor envi-ronments [3], in wireless communication the communicationrange estimation mainly depends on the radio propagationmodel assumed. Although choosing an appropriate propa-gation model depends on the current scenario, e.g., if it isurban or a highway, we briefly show in the following howthe outage probability can be used to estimate the transmis-sion range when the simple log-normal shadowing model isassumed [5]. According to this model, the received power indB is:

[Pr]dB = [Pr]dB +KdB − 10γ log10 (d/d0)− ψdB, (3)

where Pr is the received power at distance d; Pt is the trans-mit power (included in the sender’s message); KdB is theunit power loss in dB which depends on the antenna prop-erties of the transceivers; γ is the path loss coefficient whichdepends on the radio environment; d0 is the reference dis-tance; ψdB is a Gauss distributed random variable with zeromean and with σ2

ψdBas the variance generated due to the

shadowing effect. We consider the channel as either slowfading or as very fast fading. Slow fading and very fast fad-ing channels have almost the same performance as additivewhite Gaussian noise (AWGN) channels. To analyze theperformance of wireless communication in AWGN we haveto consider two criteria of interest: the bit error probabilityand the outage probability. In AWGN, for the BPSK mod-ulation the bit error probability is defined as:

Pb = Q(√

2γ0). (4)

The outage probability Pout is the probability that the re-ceived signal’s average SNR γs falls below the minimum re-quired SNR for the pre-defined acceptable communicationperformance γ0 [5, 7]. Mathematically,

Pout = p (γs < γ0) =

∫ γ0

0

pγ0 (γ) dγ. (5)

From Equations 4 & 5 the required average SNR is:

γs =

(Q−1 (Pb)

)2−2 ln (1− Pout)

. (6)

In AWGN, the carrier-to-noise ratio of the received signal is:

(C

N

)dB

=

[EbN0

]dB

+

[fbB

]dB

, (7)

where EbN0

= γb is SNR per bit; fb is the channel data

rate (net bitrate); and B is the channel bandwidth [5]. AsCdB = 10 log (Pr) and for the BPSK modulation γb = γs,Equation 7 can be re-written as:

[Pr]dB = [γs]dB +

[fbB

]dB

+NdB (8)

Finally, using Equations 3,6 & 8 the transmission range canbe estimated by:

d = d0 × 10

[Pt]dB+KdB−

⎡⎢⎣ (Q−1(Pb))

2

−2 ln(1−Pout)

⎤⎥⎦dB

−[fbB

]dB

−NdB

10γ (9)

In wireless transceiver design, a typical BER of 10−4 and2% outage probability are considered acceptable in perfor-mance. Figure 4 shows estimated transmission range valuesfor increasing outage probabilities. In this work, we assumein our simulation, transmission range values with 2% of out-age probability. Thus, values are approximately 100, 150,200 and 300 meters for power values of 300, 800, 2800 and10000 mW, respectively.

0 0.01 0.02 0.03 0.04 0.050

50

100

150

200

250

300

350

400

Pout

Dis

tanc

e [m

]

Range estimation for different transmit power[K

dB = −40dB, f

b = 6Mbps, B = 10MHz,

BER = 10−4, γ = 3.5, σ = 2dB, N0 = −110dBm]

Pt = 300 mW

Pt = 800 mW

Pt = 2800 mW

Pt = 10000 mW

Figure 4: Transmission range estimation for increas-ing accepted outage probabilities and power levels.

4. PERFORMANCE EVALUATIONThe performance evaluation of DOT is carried out by

means of simulations. Our goal is to study the scalabilityof DOT under diverse scenarios by comparing it with twostate-of-the-art suppression techniques, namely:

- Slotted 1-Persistence: it is the mechanism that achievedbest performance in terms of end-to-end delay among thetwo slotted schemes proposed in [29].

- Optimized Slotted 1-Persistence: it relies on an op-timized version of the Slotted 1-Persistence suppressionmethod to prevent nearly simultaneous rebroadcasts in asingle time slot in dense networks [16].

We utilize the MiXiM Framework1 and adjust the avail-able implementation of the IEEE 802.11b protocol to comply

1http://mixim.sourceforge.net

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with basic specifications of the 802.11p version. Table 1 con-tains a summary of the simulation parameters. In the MAClayer, we set the bit rate to 6 Mbit/s, the Contention Win-dow (CW) to values between 15 and 1023, the slot time to 13μs, the SIFS to 32 μs, and the DIFS to 58 μs. In the phys-ical layer, we operate on the 5.9 GHz frequency band, with10 MHz of bandwidth. Based on our estimates in Section3.3, we set the transmission power to 800 mW to achieve ap-proximately 150 meters of communication range with outageprobability of 2%. We use the Friis Free Space Path Loss(FSPL) propagation model with exponent α equal to 3.5, asit is within the range 2.7 to 5, estimated for outdoor shad-owed urban areas in [15]. In addition, we include shadowingeffects that are modeled following a log-normal distributionwith zero mean and standard deviation σ = 6.25 dB, as itis within the range 4 to 12 dB for outdoor propagation con-ditions according to [15]. The modulation used is the oneprovided by the Veins project2, which is based on measure-ments from [17] for the 6 Mbit/s bitrate.

For all suppression mechanisms, we set the slot time stto 5 ms. We define the total number of time slots for Slot-ted 1-Persistence NSstd to 3 and for Optimized Slotted 1-Persistence we set NSopt to 6 (3 slots for each road direc-tion as defined in [16]). The value chosen for Slotted 1-Persistence is based on simulation parameters used in [20].The maximum additional delay Dmax used by OptimizedSlotted 1-Persistence is set to 1 ms. Finally, for the DOTmechanism we set the time slot density tsd to 1 and addi-tional delay d to DIFS.

For all simulation scenarios the message size is 2312 byteslarge, the maximum allowed by the 802.11p standard. Datamessages are generated every 2 seconds, i.e., message fre-quency of 0.5 Hz. Each message is generated by one fixedvehicle positioned in one end of the road and gathered byanother fixed vehicle in the other end of road. For each simu-lation scenario 20 runs of 100 seconds are executed. Finally,beacons are 24 bytes large and sent at 1 Hz. The choicefor such beaconing rate is based on the fact that vehiclesmove in the simulation only once per second. Therefore,neighbors’ information would not be improved with a higherbeaconing rate. Furthermore, varying the beaconing rate inearly experiments has not led to significant changes in oursimulation results.

2http://veins.car2x.org/

Physical Layer

Frequency Band 5.9 GHzBandwidth 10 MHz

Transmission Range ∼150 mFSPL exponent α 3.5Log-normal σ 6.25 dBModulation Based on [17]

Link Layer

Bit Rate 6 Mbit/sCW [15,1023]

Slot Time 13 μsSIFS 32 μsDIFS 58 μsst 5 mstsd 1d DIFS

Supression NSstd 3Mechanisms NSopt 6

Dmax 1 msBeacon Size 24 Bytes

Beacon Frequency 1Hz

Scenarios

Data Message Size 2312 BytesData Message Freq. 0.5 HzNetwork Density 50 veh./km/lane

# Runs 20

Table 1: Simulation parameters

We consider a scenario with a 1-kilometer straight high-way with two lanes in each road direction. This scenariowas created with SUMO [9]. Therefore, it includes realisticmobility patterns such as vehicle overtaking, lane changing,and relies on the well-known car-following mobility model.Vehicles’ speeds vary according to the density consideredby following the Krauß mobility model, i.e., the higher thedensity is, the slower vehicles move.

Our evaluation considers the following metrics:

- Delivery ratio: the percentage of messages generated bythe farthest vehicle in one end of the road which fullypropagate and are received by a vehicle in the extremeopposite end of the road. Ideally, dissemination protocolsmust achieve a delivery ratio percentage close to 100% indense networks.

- Delay : the total time taken for a message to propagatefrom one end to the other of the road length. This is par-ticularly important for critical safety messages that mustbe disseminated as quickly as possible. We additionallycompare the performance of each protocol with a theoret-ical optimum which serves as lower bound. This value is

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Figure 6: Results with 95% confidence intervals for different transmission range settings

simply calculated as the minimum number of hops that amessage must travel times the transmission delay, giventhe transmission range employed.

- Total number of transmissions: the total number oftransmissions performed on average by an arbitrary ve-hicle. We consider only data messages in these results,thereby excluding transmissions of beacons. This value isnormalized by the total number of vehicles in each sce-nario. In order to be scalable, protocols must keep a lownumber of transmissions during a message’s dissemination.

4.1 Network densityWe first study the performance of the protocols with in-

creasing network densities. Since we focus on dense net-works, we fix the parameters in Table 1 and vary the densityfrom 20 to 100 vehicles/km/lane.

As shown in Figure 5(a), Slotted 1-Persistence improvesits delivery ratio up to 60% as the network density increases.This is explained by the extra rebroadcast redundancy whichoccurs when more vehicles are assigned to a single time slot.In contrast, DOT maintains performance of near 100% forall density values, whereas Optimized Slotted 1-Persistencereaches 100% up to density of 60 vehicles/km/lane andsuffers a decrease of up to 10% with density of 100 vehi-cles/km/lane.

Figure 5(b) shows the performance with respect to theend-to-end delay. The end-to-end delay tends to increasewith density for protocols that rely on a fixed number of timeslots such as Slotted 1-Persistence and Optimized Slotted 1-Persistence. This can be reasoned by the higher contentiondelay generated when more vehicles attempt to rebroadcastin a single time slot. In contrast, DOT scales properly withincreasing densities. In fact, the higher the density of thenetwork, the higher the chance is that a vehicle is positionedcloser to the border of the transmission range. Thus, delayvalues with DOT are close to the theoretical optimum indensities ranging from 30 to 100 vehicles/km/lane.

The number of transmissions performed by Slotted 1-Persistence and Optimized Slotted 1-Persistence also in-creases with higher densities, as shown in Figure 5(c). Thisis due to the higher number of vehicles positioned in thegeographical region corresponding to a single time slot. Byrelying on the control of time slot density, DOT scales prop-erly with higher densities. In fact, in proportion with the

total number vehicles in each density, the number of trans-missions tends to decrease.

In general, DOT scales more efficiently with increasingnetwork densities when compared with traditional methodsthat employ fixed time slots such as Slotted 1-Persistenceand Optimized Slotted 1-Persistence.

4.2 Transmission rangeAnother important aspect is the performance of protocols

when different transmission ranges are employed by vehicles.Specially for approaches that employ a fixed number of timeslots, increasing the transmission range affects directly thesize of each time slot and, thus, the performance of protocols.In addition, it affects the number of hops required for a mes-sage to travel the complete highway considered. We fix theparameters in Table 1 and vary the transmission range from100 to 300 meters. Additionally, we consider the scenariomix where different vehicles employ different transmissionranges. More specifically, each of the ranges 100, 150, 200and 300 meters is used by 25% of the vehicles. Each vehicletakes a range value in the beginning of the simulation runand employ it until the simulation ends.

Figure 6(a) shows the performance of protocols with re-spect to the delivery ratio. Both Optimized Slotted 1-Persistence and DOT protocols achieve near 100% in ev-ery transmission range setting. In contrast, Slotted 1-Persistence shows higher delivery ratio when consideringhigher transmission range values. This is explained by theextra rebroadcast redundancy and fewer hops needed for amessage to be fully disseminated when higher transmissionranges are employed. Furthermore, Slotted 1-Persistence isaffected when different ranges are employed by different ve-hicles, which can also be explained by the higher number ofhops required on average for a message’s dissemination.

With fewer hops needed for a message to travel, the end-to-end delay presented by each protocol also decreases whenhigher transmission ranges are employed (Figure 6(b)). DOTpresents the lowest delay, with values near the theoreticaloptimum for each range setting.

Figure 6(c) shows the total number of transmissions per-formed by each protocol. Since Slotted 1-Persistence andOptimized Slotted 1-Persistence adopt a fixed time slot ap-proach, higher transmission ranges means more vehicles as-signed to a single time slot. Therefore, more rebroadcast

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Figure 7: Results with 95% confidence intervals for different time slot parameters used by each protocol

redundancy and thus more transmissions are expected. Onthe other hand, DOT controls the time slot density regard-lessly of the current density of vehicles within the trans-mission range. Therefore, fewer hops is translated to fewertransmissions.

Results show that not only DOT scales properly with in-creasing and heterogeneous transmission range settings, butalso achieves near optimum performance in terms of end-to-end delay.

4.3 Time slot parameterIn this section, we analyze the performance of protocols

when varying their main parameters, namely, the total num-ber of time slots tsn (used by Slotted 1-Persistence and Op-timized Slotted 1-Persistence) and the time slot density tsd(used by DOT). Other parameters are fixed as shown in Ta-ble 1. In particular, Optimized Slotted 1-Persistence usesdoubled values of tsn to distribute the number of time slotsequally among the two road directions, as detailed in [16].

With regard to the delivery ratio, both Optimized Slotted1-Persistence and Slotted 1-Persistence achieve higher deliv-ery ratio when increasing the total number of time slots, asshown in Figure 7(a). In fact, employing more time slotsleads to a lower number of vehicles assigned to a single timeslot. Therefore, a lower level of rebroadcast redundancyis expected and messages can travel with less interferencethroughout the road length. The opposite effect occurs whenthe time slot density is increased in DOT. Higher values fortsd means more vehicles within a single time slot, whichleads to a decrease in delivery ratio from tsd = 4 in thisscenario.

Equivalently to what occurs when varying the networkdensity, there is an increase in delay when more vehicles at-tempt to transmit nearly simultaneously in a single time slot(Figure 7(b)). This occurs when decreasing tsn (OptimizedSlotted 1-Persistence and Slotted 1-Persistence) or increas-ing tsd (DOT). Such increase in the number of transmissionscan be confirmed in Figure 7(c). In general, the increase indelay is upper bounded by the network density in the sce-nario considered, which consequently limits the maximumnumber of vehicles that are within the transmission range of150 meters.

In general, all protocols perform best when fewer vehi-cles attempt to transmit nearly simultaneously. This means

tsd = 1 for DOT and tsn = 8 for Optimized Slotted1-Persistence and Slotted 1-Persistence. However, whilefinding the optimal value for tsn in Optimized Slotted 1-Persistence and Slotted 1-Persistence depends on accuratelyknowing the current network density, DOT with tsd = 1scales independently from other factors.

4.4 Transmission range errorAll protocols considered in our evaluation depend on ac-

curately estimating which vehicles are within the sender’stransmission range in order to distribute time slots amongvehicles efficiently. As discussed in Section 3.3, due toa certain error probability in the wireless communicationand inaccurate positioning estimation (GPS), the transmis-sion range might be either underestimated or overestimated.Thus, we study the effects of such errors on the performanceof each protocol. With an outage probability of 2%, thecentral point zero in the x-axis represents an accurate es-timation of the transmission range, which is approximately150 meters. Negative and positive values in the x-axis areunderestimated and overestimated percentage values withrespect to point zero, respectively. Other parameters arefixed as shown in Table 1. We additionally consider resultsof running DOT with tsd = 2 and tsd = 3.

Figure 8(a) shows the performance of protocols with re-spect to the delivery ratio. For all protocols, an inaccuratetransmission range estimation may result in vehicles beingassigned to a sub-optimal time slot. Nevertheless, every ve-hicle still schedules a rebroadcast, which helps prevent thedissemination of messages from being stopped. When thetransmission range is underestimated, time slots are mappedto smaller geographical regions. However, vehicles posi-tioned beyond the underestimated range still receive andrebroadcast messages. This leads to a high level of trans-mission redundancy in a single time slot and, thus, to alower delivery ratio down to 5% when the complete range isunderestimated.

The results with respect to the end-to-end delay are shownin Figure 8(b). For protocols relying on fixed time slots suchas Optimized Slotted 1-Persistence and Slotted 1-Persistence,changing the boundary of the time slots does not consider-ably affect the expected end-to-end delay. With a density of50 vehicles/km/lane used in this scenario, the chance thatat least one vehicle is positioned in the geographical region

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Figure 8: Results with 95% confidence intervals for increasing underestimation and overestimation errors inthe transmission range estimation

mapping the earliest time slot is high. One variation thatcan be observed in these protocols is with regard to the num-ber of transmissions (Figure 8(c)). With underestimatedrange values, more vehicles positioned beyond the underes-timated range are assigned to the earliest time slot, therebyresulting in more transmissions.

In contrast, inaccurate range estimations directly affectthe expected end-to-end delay in DOT. As discussed in Sec-tion 3.3, to prevent an increase in number of transmissionswhen the transmission range in underestimated, vehicles po-sitioned beyond the estimated range border are placed in theback of the sorted list �v. This results in increasing the end-to-end delay, as all vehicles will rely on such underestimatedrange and, thus, more hops will be needed for a message tobe fully disseminated along the road. For underestimatedvalues higher than 60%, the end-to-end delay starts to de-crease as a consequence of the lower delivery ratio presentin this range for all protocols. On the other hand, higherdelay values can also be expected with an overestimationof the transmission range, since vehicles may unnecessarilyexpect other vehicles farther in the message direction to re-broadcast. When more vehicles are assigned to a single timeslot, i.e., tsd > 1, both effects can be minimized as shownin Figure 8(b). This is explained by the higher chance thatan inaccurate estimation is compensated by another vehiclealso assigned to the same time slot but positioned fartheror nearer the sender. Although the number of transmissionsalso increases with higher underestimated ranges, the valuesachieved are considerably lower when compared with Op-timized Slotted 1-Persistence and Slotted 1-Persistence asshown in Figure 8(c).

Results show that overestimating values for the transmis-sion range is less harmful for all protocols with regard todelivery ratio and number of transmissions. For all levels ofestimation errors, DOT presents better performance resultswith regard to delivery ratio and number of transmissions.Despite the effects of inaccurate range estimations, DOTstill presents lower end-to-end delay values compared withOptimized Slotted 1-Persistence and Slotted 1-Persistenceconsidering a range of error up to 30%. Nevertheless, theseeffects are minimized when higher time slot density valuesare allowed.

5. CONCLUSION AND FUTURE WORKIn this paper, we have presented a broadcast suppression

scheme that is scalable to diverse network densities. We ad-dressed major problems in current delay-based techniquesand designed the Distributed Optimized Time (DOT) slotscheme. By exploiting the presence of 1-hop neighbor in-formation contained in periodic safety beacons, DOT is ca-pable of controlling with high precision the density of ve-hicles within each time slot. By means of simulations, weshowed that DOT is scalable, achieves near optimum de-lay results, and is robust to errors caused by possible inac-curate transmission range estimations. Furthermore, DOToutperformed other delay-based schemes in diverse networkdensities. In future work, we will aim to incorporate DOTin a store-carry-forward scheme which is suitable for bothhighway and urban scenarios.

6. ACKNOWLEDGMENTSThis work is supported by iLAND Project, ARTEMIS-

2008-1, Project contract no. 100026; and by SI4MS projectNWO/STW grant (dossier 655.010.209).

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